Online steel component automatic analysis method based on X-ray fluorescence spectrometer

Abstract

The invention relates to an on-line steel component automatic analysis method based on an X-ray fluorescence spectrometer, and the method comprises the following steps: M1, carrying out on-line detection on steel, obtaining data information of the temperature of the steel in real time based on an infrared thermometer, and obtaining data information of element analysis of the steel in real time based on the X-ray fluorescence spectrometer; the data information of the measurement position of the X-ray fluorescence spectrophotometer is obtained in real time based on a laser sensor, the content of each element in the steel is predicted through an improved whale optimization lightweight gradient elevator classification prediction algorithm, and the predicted data information of the content of each element in the steel is obtained. According to the invention, component analysis operation is automatically and standardly carried out, the use is simple and convenient, online component automatic detection of steel is realized, the working efficiency is improved, the labor cost is reduced, and the operation safety is improved.

Description

Technical Field

[0001] The invention relates to the technical field of automatic steel analysis, and in particular to an online automatic steel composition analysis method based on an X-ray fluorescence spectrometer. Background Art

[0002] When steel companies deliver bars, profiles, building materials and other products, they will identify the brand of the products, that is, test the elemental composition of the steel itself, so as to avoid product confusion, substandard composition, etc. At present, many companies use handheld X-ray fluorescence spectrometers for manual testing, which has the following disadvantages: no protective device, which will cause certain harm to the human body; easy to make mistakes; handheld measurement, which is prone to measurement errors; handheld equipment is heavy and inconvenient to operate; manual reading of data is prone to errors, and data storage, collection, transmission, and analysis are inconvenient. At the same time, the temperature of the steel itself and the changes in the measurement position may affect the accuracy of XRF analysis. Summary of the invention

[0003] In view of the above problems, the present invention provides an online automatic steel composition analysis method based on X-ray fluorescence spectrometer, which not only automates and standardizes the composition analysis operation, is easy to use, and realizes automatic online composition detection of steel, but also improves work efficiency, reduces labor costs, and improves operation safety.

[0004] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows: An online automatic steel composition analysis method based on an X-ray fluorescence spectrometer, the method comprising: M1. Steel is tested online, and data information of steel temperature is obtained in real time based on infrared thermometer, data information of elemental analysis of steel is obtained in real time based on X-ray fluorescence spectrometer, and data information of measurement position of X-ray fluorescence spectrometer is obtained in real time based on laser sensor; M2. Based on the data information of the temperature of the steel, the data information of the element analysis of the steel and the data information of the measurement position of the X-ray fluorescence spectrometer, the content of each element in the steel is predicted using an improved classification prediction algorithm based on whale optimization lightweight gradient boosting machine to obtain the data information of the content of each element in the steel after prediction; M3. Based on the predicted data information of the content of each element in the steel, the content of each element in the steel is optimized by using an improved Archimedean optimization algorithm based on attenuation factor and dynamic learning to obtain data information of the content of each element in the optimized steel; M4. Based on the data information of the content of each element in the optimized steel, a steel quality assessment function R is constructed to calculate the quality assessment value of the steel to obtain data information of the quality assessment value of the steel.

[0005] Furthermore, the steel quality evaluation function R is: , Among them, x i is the data information of the content of the i-th element in the optimized steel, n is the sample size, α i is the weight coefficient of the content of the i-th element in the optimized steel.

[0006] Furthermore, the weight coefficient α of the content of the i-th element in the optimized steel is i The constraints are: .

[0007] Furthermore, the method further comprises: M5. Based on the data information of the quality assessment value of the steel, a preset threshold is set. If the quality assessment value of the steel is less than the preset threshold, the steel does not meet the requirements and is an unqualified product. If the quality assessment value of the steel is greater than the preset threshold, the steel meets the requirements and is a qualified product.

[0008] Furthermore, in step M2, the use of the improved whale-optimized lightweight gradient boosting machine classification prediction algorithm to predict the content of each element in the steel includes: M21. Inputting the data information of the temperature of the steel, the data information of the elemental analysis of the steel, and the data information of the measurement position of the X-ray fluorescence spectrometer into the lightweight gradient boosting machine classification prediction model for training and learning, initializing the hyperparameters of the model, and obtaining the data information of the hyperparameters of the initialized model; M22. Based on the data information of the hyperparameters of the initialized model, the whale population is initialized, the population parameters are determined, and the data information of the initialized whale population is obtained; M23. Based on the data information of the initialized whale population, establish the fitness function Q of the population individuals, , Among them, x is the data information of the whale population after initialization, α1, α2 and α3 are the dynamic adjustment factors of the fitness of the individuals in the whale population, and the fitness values ​​of the individuals in the whale population are calculated to obtain the data information of the fitness values ​​of the individuals in the whale population; M24. Based on the data information of the fitness values ​​of the whale population individuals, establish the target optimization function W of the population, , Among them, y is the data information of the fitness value of the individual whale population, β1, β2 and β3 are the target optimization factors, and the hyperparameters of the initialized model are optimized to obtain the data information of the hyperparameters of the optimized model; M25. Inputting the data information of the hyperparameters of the optimized model into the lightweight gradient boosting machine classification prediction model for training to obtain a trained lightweight gradient boosting machine classification prediction model; M26. Based on the trained lightweight gradient boosting machine classification prediction model, the data information of the temperature of the steel, the data information of the elemental analysis of the steel and the data information of the measurement position of the X-ray fluorescence spectrometer are input to predict the content of each element in the steel, and obtain the data information of the predicted content of each element in the steel.

[0009] Furthermore, the target optimization factors β1, β2 and β3 are, , , , Among them, y is the data information of the fitness value of the individual whale population.

[0010] Furthermore, the fitness dynamic adjustment factors α1, α2 and α3 of the individual whale population are: , , , Among them, x is the data information of the initialized whale population.

[0011] Furthermore, in step M3, the optimization of the content of each element in the steel using the improved Archimedean optimization algorithm based on attenuation factor and dynamic learning includes: M31. Based on the data information of the content of each element in the predicted steel, the Archimedean population is initialized, the population parameters and the maximum number of iterations K are determined, and the data information of the initialized Archimedean population is obtained; M32. Based on the data information of the initialized Archimedean population, establish a fitness function G of the population individuals based on the attenuation factor, , , , , Among them, z is the data information of the initialized Archimedes population, δ1, δ2 and δ3 are attenuation factors, and the fitness values ​​of the individuals in the Archimedes population are calculated to obtain the data information of the fitness values ​​of the individuals in the Archimedes population; M33. Based on the data information of the fitness values ​​of the individuals in the Archimedean population, an optimization function H based on a dynamic learning factor is established. , , , , Among them, r is the data information of the fitness value of the Archimedean population individual, λ1, λ2 and λ3 are dynamic learning factors, and the content of each element in the steel is optimized to obtain the data information of the content of each element in the optimized steel.

[0012] Furthermore, the constraints of the dynamic learning factors λ1, λ2 and λ3 are: .

[0013] In order to achieve the above-mentioned object and other related objects, the present invention also provides a system for realizing any one of the above-mentioned methods for automatic online steel composition analysis based on X-ray fluorescence spectrometer, the system comprising: A data acquisition module, used to acquire data information of the temperature of the steel in real time based on an infrared thermometer, acquire data information of elemental analysis of the steel in real time based on an X-ray fluorescence spectrometer, and acquire data information of the measurement position of the X-ray fluorescence spectrometer in real time based on a laser sensor; A prediction module for element content in steel, connected to the data acquisition module, is used to predict the content of each element in the steel using an improved classification prediction algorithm based on whale optimization lightweight gradient boosting machine to obtain data information on the content of each element in the steel after prediction; An optimization module for element content of steel, connected to the prediction module for element content of steel, is used to optimize the content of each element in the steel by using an improved Archimedean optimization algorithm based on attenuation factor and dynamic learning, and obtain data information on the content of each element in the optimized steel; A steel quality assessment module, connected to the steel element content optimization module, is used to construct a steel quality assessment function R, calculate the quality assessment value of the steel, and obtain data information of the quality assessment value of the steel; The steel quality identification module is connected to the steel quality evaluation module and is used to set a preset threshold. If the quality evaluation value of the steel is less than the preset threshold, the steel does not meet the requirements and is an unqualified product. If the quality evaluation value of the steel is greater than the preset threshold, the steel meets the requirements and is a qualified product.

[0014] The present invention has the following positive effects: 1. The present invention predicts the content of each element in steel by adopting an improved whale-optimized lightweight gradient boosting machine classification prediction algorithm, and optimizes the content of each element in steel by combining an improved Archimedes optimization algorithm based on attenuation factor and dynamic learning. It not only automates and standardizes the component analysis operation, is easy to use, realizes automatic online component detection of steel, but also improves work efficiency, reduces labor costs, and improves operation safety.

[0015] 2. The present invention constructs a steel quality assessment function R to calculate the quality assessment value of the steel, and combines the setting of preset thresholds to accurately and intelligently detect the quality of the steel. This not only improves the reliability and security of the data, avoids human misjudgment, data tampering, etc., and ensures the quality of the products produced, but also realizes full-process automated operation, relies on computer terminals for control and automatic judgment, and the data can be directly transmitted to the central control platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a flow chart of the improved whale-optimized lightweight gradient boosting machine classification prediction algorithm of the present invention; Figure 3 It is a flow chart of the improved Archimedean optimization algorithm based on attenuation factor and dynamic learning of the present invention; Figure 4 It is a schematic diagram of the system framework of the present invention; Figure 5 It is a structural schematic diagram of the detection device of the present invention; Figure 6 This is the overall architecture diagram of the online automatic analysis system of the X-ray fluorescence spectrometer of the present invention.

[0017] Explanation of the numbers in the figure: 1-infrared thermometer, 2-X-ray fluorescence spectrometer, 3-laser sensor. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0019] Example 1: Figure 1 or Figure 5 As shown, an online steel composition automatic analysis method based on X-ray fluorescence spectrometer, the method comprising: M1. Steel is tested online, and data information of the temperature of the steel is obtained in real time based on the infrared thermometer 1, data information of the elemental analysis of the steel is obtained in real time based on the X-ray fluorescence spectrometer 2, and data information of the measurement position of the X-ray fluorescence spectrometer is obtained in real time based on the laser sensor 3; M2. Based on the data information of the temperature of the steel, the data information of the element analysis of the steel and the data information of the measurement position of the X-ray fluorescence spectrometer, the content of each element in the steel is predicted using an improved classification prediction algorithm based on whale optimization lightweight gradient boosting machine to obtain the data information of the content of each element in the steel after prediction; M3. Based on the predicted data information of the content of each element in the steel, the content of each element in the steel is optimized by using an improved Archimedean optimization algorithm based on attenuation factor and dynamic learning to obtain data information of the content of each element in the optimized steel; M4. Based on the data information of the content of each element in the optimized steel, a steel quality assessment function R is constructed to calculate the quality assessment value of the steel to obtain data information of the quality assessment value of the steel.

[0020] In this embodiment, the steel quality evaluation function R is: , Among them, x i is the data information of the content of the i-th element in the optimized steel, n is the sample size, α i is the weight coefficient of the content of the i-th element in the optimized steel.

[0021] In this embodiment, the weight coefficient α of the content of the i-th element in the optimized steel is i The constraints are: .

[0022] In this embodiment, the method further includes: M5. Based on the data information of the quality assessment value of the steel, a preset threshold is set. If the quality assessment value of the steel is less than the preset threshold, the steel does not meet the requirements and is an unqualified product. If the quality assessment value of the steel is greater than the preset threshold, the steel meets the requirements and is a qualified product.

[0023] In this embodiment, if Figure 2 As shown, in step M2, the use of the improved whale-optimized lightweight gradient boosting machine classification prediction algorithm to predict the content of each element in the steel includes: M21. Inputting the data information of the temperature of the steel, the data information of the elemental analysis of the steel, and the data information of the measurement position of the X-ray fluorescence spectrometer into the lightweight gradient boosting machine classification prediction model for training and learning, initializing the hyperparameters of the model, and obtaining the data information of the hyperparameters of the initialized model; M22. Based on the data information of the hyperparameters of the initialized model, the whale population is initialized, the population parameters are determined, and the data information of the initialized whale population is obtained; M23. Based on the data information of the initialized whale population, establish the fitness function Q of the population individuals, , Among them, x is the data information of the whale population after initialization, α1, α2 and α3 are the dynamic adjustment factors of the fitness of the individuals in the whale population, and the fitness values ​​of the individuals in the whale population are calculated to obtain the data information of the fitness values ​​of the individuals in the whale population; M24. Based on the data information of the fitness values ​​of the whale population individuals, establish the target optimization function W of the population, , Among them, y is the data information of the fitness value of the individual whale population, β1, β2 and β3 are the target optimization factors, and the hyperparameters of the initialized model are optimized to obtain the data information of the hyperparameters of the optimized model; M25. Inputting the data information of the hyperparameters of the optimized model into the lightweight gradient boosting machine classification prediction model for training to obtain a trained lightweight gradient boosting machine classification prediction model; M26. Based on the trained lightweight gradient boosting machine classification prediction model, the data information of the temperature of the steel, the data information of the elemental analysis of the steel and the data information of the measurement position of the X-ray fluorescence spectrometer are input to predict the content of each element in the steel, and obtain the data information of the predicted content of each element in the steel.

[0024] In this embodiment, the target optimization factors β1, β2 and β3 are, , , , Among them, y is the data information of the fitness value of the individual whale population.

[0025] In this embodiment, the fitness dynamic adjustment factors α1, α2 and α3 of the whale population individuals are: , , , Among them, x is the data information of the initialized whale population.

[0026] Example 2: Based on the online automatic steel composition analysis method based on X-ray fluorescence spectrometer in Example 1, the present invention is further illustrated and described below.

[0027] like Figure 1 or Figure 5 As shown, an online steel composition automatic analysis method based on X-ray fluorescence spectrometer, the method comprising: M1. Steel is tested online, and data information of the temperature of the steel is obtained in real time based on the infrared thermometer 1, data information of the elemental analysis of the steel is obtained in real time based on the X-ray fluorescence spectrometer 2, and data information of the measurement position of the X-ray fluorescence spectrometer is obtained in real time based on the laser sensor 3; M2. Based on the data information of the temperature of the steel, the data information of the element analysis of the steel and the data information of the measurement position of the X-ray fluorescence spectrometer, the content of each element in the steel is predicted using an improved classification prediction algorithm based on whale optimization lightweight gradient boosting machine to obtain the data information of the content of each element in the steel after prediction; M3. Based on the predicted data information of the content of each element in the steel, the content of each element in the steel is optimized by using an improved Archimedean optimization algorithm based on attenuation factor and dynamic learning to obtain data information of the content of each element in the optimized steel; M4. Based on the data information of the content of each element in the optimized steel, a steel quality assessment function R is constructed to calculate the quality assessment value of the steel to obtain data information of the quality assessment value of the steel.

[0028] In this embodiment, if Figure 3 As shown, in step M3, the optimization of the content of each element in the steel using the improved Archimedean optimization algorithm based on attenuation factor and dynamic learning includes: M31. Based on the data information of the content of each element in the predicted steel, the Archimedean population is initialized, the population parameters and the maximum number of iterations K are determined, and the data information of the initialized Archimedean population is obtained; M32. Based on the data information of the initialized Archimedean population, establish a fitness function G of the population individuals based on the attenuation factor, , , , , Among them, z is the data information of the initialized Archimedes population, δ1, δ2 and δ3 are attenuation factors, and the fitness values ​​of the individuals in the Archimedes population are calculated to obtain the data information of the fitness values ​​of the individuals in the Archimedes population; M33. Based on the data information of the fitness values ​​of the individuals in the Archimedean population, an optimization function H based on a dynamic learning factor is established. , , , , Among them, r is the data information of the fitness value of the Archimedean population individual, λ1, λ2 and λ3 are dynamic learning factors, and the content of each element in the steel is optimized to obtain the data information of the content of each element in the optimized steel.

[0029] In this embodiment, the constraints of the dynamic learning factors λ1, λ2 and λ3 are: .

[0030] In this embodiment, if Figure 4 As shown, the present invention provides a system for implementing any of the above-mentioned methods for automatic online steel composition analysis based on an X-ray fluorescence spectrometer, the system comprising: A data acquisition module, used to acquire data information of the temperature of the steel in real time based on an infrared thermometer, acquire data information of elemental analysis of the steel in real time based on an X-ray fluorescence spectrometer, and acquire data information of the measurement position of the X-ray fluorescence spectrometer in real time based on a laser sensor; A prediction module for element content in steel, connected to the data acquisition module, is used to predict the content of each element in the steel using an improved classification prediction algorithm based on whale optimization lightweight gradient boosting machine to obtain data information on the content of each element in the steel after prediction; An optimization module for element content of steel, connected to the prediction module for element content of steel, is used to optimize the content of each element in the steel by using an improved Archimedean optimization algorithm based on attenuation factor and dynamic learning, and obtain data information on the content of each element in the optimized steel; A steel quality assessment module, connected to the steel element content optimization module, is used to construct a steel quality assessment function R, calculate the quality assessment value of the steel, and obtain data information of the quality assessment value of the steel; The steel quality identification module is connected to the steel quality evaluation module and is used to set a preset threshold. If the quality evaluation value of the steel is less than the preset threshold, the steel does not meet the requirements and is an unqualified product. If the quality evaluation value of the steel is greater than the preset threshold, the steel meets the requirements and is a qualified product.

[0031] In this embodiment, if Figure 6 As shown, the motion module: the entire motion module is composed of a horizontal and two vertical modules. The horizontal motion module carries the horizontal movement of all functional parts, and uses the limit mechanism to determine the starting and ending points of the reciprocating stroke; the vertical module is responsible for the up and down movement of the sample surface treatment device; the vertical module is responsible for the up and down movement of the clamping device, the distance measuring device, and the X-ray fluorescence spectrometer.

[0032] Cooling device: Since the online bar temperature is high, it is necessary to cool it down between measurements. The cooling device consists of a blower and an air knife, and the bar is cooled down through PLC control.

[0033] Temperature measuring device: The temperature measuring device uses infrared temperature measurement to measure the temperature of the online bars to determine whether the temperature has dropped to the set value.

[0034] Distance measuring device: The distance measuring device uses laser distance measurement to measure the surface position of bars of various sizes and specifications. The data is used to control the grinding depth of surface treatment and the measurement position of the X-ray fluorescence spectrometer.

[0035] Clamping device: After locating the bar to be measured through the cooperation of the distance measuring device and the horizontal motion module, the clamping device uses a cylinder to clamp the bar to prevent the bar from loosening during surface grinding.

[0036] Surface treatment device: The surface treatment device removes the oxide scale on the surface of the bar by grinding with a grinding wheel, making the measured data more accurate.

[0037] X-ray fluorescence spectrometer: The X-ray fluorescence spectrometer is handheld and remotely controlled by PLC. The measured data is exchanged through wireless transmission.

[0038] The present invention also provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the methods for automatic online steel composition analysis based on an X-ray fluorescence spectrometer.

[0039] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0040] In summary, the present invention not only performs component analysis operations in an automated and standardized manner, is easy to use, and realizes automatic online component detection of steel, but also improves work efficiency, reduces labor costs, and improves operation safety.

[0041] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An online automatic steel composition analysis method based on X-ray fluorescence spectrometer, characterized in that: The method comprises: M1. Steel is tested online, and data information of steel temperature is obtained in real time based on infrared thermometer, data information of elemental analysis of steel is obtained in real time based on X-ray fluorescence spectrometer, and data information of measurement position of X-ray fluorescence spectrometer is obtained in real time based on laser sensor; M2. Based on the data information of the temperature of the steel, the data information of the element analysis of the steel and the data information of the measurement position of the X-ray fluorescence spectrometer, the content of each element in the steel is predicted using an improved classification prediction algorithm based on whale optimization lightweight gradient boosting machine to obtain the data information of the content of each element in the steel after prediction; M3. Based on the predicted data information of the content of each element in the steel, the content of each element in the steel is optimized by using an improved Archimedean optimization algorithm based on attenuation factor and dynamic learning to obtain data information of the content of each element in the optimized steel; M4. Based on the data information of the content of each element in the optimized steel, a steel quality assessment function R is constructed to calculate the quality assessment value of the steel to obtain data information of the quality assessment value of the steel.

2. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 1 is characterized in that: The steel quality evaluation function R is: ， Among them, x i is the data information of the content of the i-th element in the optimized steel, n is the sample size, α i is the weight coefficient of the content of the i-th element in the optimized steel.

3. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 2 is characterized in that: The weight coefficient α of the content of the i-th element in the optimized steel is i The constraints are: 。 4. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 1, characterized in that: The method further comprises: M5. Based on the data information of the quality assessment value of the steel, a preset threshold is set. If the quality assessment value of the steel is less than the preset threshold, the steel does not meet the requirements and is an unqualified product. If the quality assessment value of the steel is greater than the preset threshold, the steel meets the requirements and is a qualified product.

5. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 1, characterized in that: In step M2, the use of the improved whale-optimized lightweight gradient boosting machine classification prediction algorithm to predict the content of each element in the steel includes: M21. Inputting the data information of the temperature of the steel, the data information of the elemental analysis of the steel, and the data information of the measurement position of the X-ray fluorescence spectrometer into the lightweight gradient boosting machine classification prediction model for training and learning, initializing the hyperparameters of the model, and obtaining the data information of the hyperparameters of the initialized model; M22. Based on the data information of the hyperparameters of the initialized model, the whale population is initialized, the population parameters are determined, and the data information of the initialized whale population is obtained; M23. Based on the data information of the initialized whale population, establish the fitness function Q of the population individuals, ， Among them, x is the data information of the whale population after initialization, α1, α2 and α3 are the dynamic adjustment factors of the fitness of the individuals in the whale population, and the fitness values ​​of the individuals in the whale population are calculated to obtain the data information of the fitness values ​​of the individuals in the whale population; M24. Based on the data information of the fitness values ​​of the whale population individuals, establish the target optimization function W of the population, ， Among them, y is the data information of the fitness value of the individual whale population, β1, β2 and β3 are the target optimization factors, and the hyperparameters of the initialized model are optimized to obtain the data information of the hyperparameters of the optimized model; M25. Inputting the data information of the hyperparameters of the optimized model into the lightweight gradient boosting machine classification prediction model for training to obtain a trained lightweight gradient boosting machine classification prediction model; M26. Based on the trained lightweight gradient boosting machine classification prediction model, the data information of the temperature of the steel, the data information of the elemental analysis of the steel and the data information of the measurement position of the X-ray fluorescence spectrometer are input to predict the content of each element in the steel, and obtain the data information of the predicted content of each element in the steel.

6. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 5 is characterized in that: The target optimization factors β1, β2 and β3 are, ， ， ， Among them, y is the data information of the fitness value of the individual whale population.

7. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 5 is characterized in that: The fitness dynamic adjustment factors α1, α2 and α3 of the individual whale population are: ， ， ， Among them, x is the data information of the initialized whale population.

8. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 1, characterized in that: In step M3, the optimization of the content of each element in the steel by using the improved Archimedean optimization algorithm based on attenuation factor and dynamic learning includes: M31. Based on the data information of the content of each element in the predicted steel, the Archimedean population is initialized, the population parameters and the maximum number of iterations K are determined, and the data information of the initialized Archimedean population is obtained; M32. Based on the data information of the initialized Archimedean population, establish a fitness function G of the population individuals based on the attenuation factor, ， ， ， ， Among them, z is the data information of the initialized Archimedes population, δ1, δ2 and δ3 are attenuation factors, and the fitness values ​​of the individuals in the Archimedes population are calculated to obtain the data information of the fitness values ​​of the individuals in the Archimedes population; M33. Based on the data information of the fitness values ​​of the individuals in the Archimedean population, an optimization function H based on a dynamic learning factor is established. ， ， ， ， Among them, r is the data information of the fitness value of the Archimedean population individual, λ1, λ2 and λ3 are dynamic learning factors, and the content of each element in the steel is optimized to obtain the data information of the content of each element in the optimized steel.

9. The method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to claim 8, characterized in that: The constraints of the dynamic learning factors λ1, λ2 and λ3 are: 。 10. A system for implementing the method for automatic online steel composition analysis based on X-ray fluorescence spectrometer according to any one of claims 1 to 9, characterized in that: The system comprises: A data acquisition module, used to acquire data information of the temperature of the steel in real time based on an infrared thermometer, acquire data information of elemental analysis of the steel in real time based on an X-ray fluorescence spectrometer, and acquire data information of the measurement position of the X-ray fluorescence spectrometer in real time based on a laser sensor; A prediction module for element content in steel, connected to the data acquisition module, is used to predict the content of each element in the steel using an improved classification prediction algorithm based on whale optimization lightweight gradient boosting machine to obtain data information on the content of each element in the steel after prediction; An optimization module for element content of steel, connected to the prediction module for element content of steel, is used to optimize the content of each element in the steel by using an improved Archimedean optimization algorithm based on attenuation factor and dynamic learning, and obtain data information on the content of each element in the optimized steel; A steel quality assessment module, connected to the steel element content optimization module, is used to construct a steel quality assessment function R, calculate the quality assessment value of the steel, and obtain data information of the quality assessment value of the steel; The steel quality identification module is connected to the steel quality evaluation module and is used to set a preset threshold. If the quality evaluation value of the steel is less than the preset threshold, the steel does not meet the requirements and is an unqualified product. If the quality evaluation value of the steel is greater than the preset threshold, the steel meets the requirements and is a qualified product.

Images